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Record W1970943829 · doi:10.1002/ieam.1324

Exploring SETAC's roles in the global dialogue on sustainability—an opening debate

2012· article· en· W1970943829 on OpenAlexaff
Ron McCormick, Larry Kapustka, Cynthia H. Stahl, Jim Fava, Emma T. Lavoie, Cory Robertson, Hans Sanderson, Heidi Scott, Tom Seager, Bruce Vigon

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsTrent University
Fundersnot available
KeywordsSession (web analytics)SustainabilityPropositionPolitical sciencePlenary sessionAdvisory committeePublic administrationPublic relationsEnvironmental ethicsLibrary scienceEngineering ethicsEngineeringBusinessComputer scienceEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

A combination platform-debate session was held at the Society of Environmental Toxicology and Chemistry (SETAC) North America annual meeting in Boston (November 2011). The session was organized by members of the Advisory Group on Sustainability, newly formed and approved as a global entity by the SETAC World Council just prior to the meeting. The platform portion of the session provided a historical backdrop for the debate that was designed to explore SETAC's role in the sustainability dialogue. The debate portion presented arguments for and against the proposition that "Science is the primary contribution of SETAC to the global dialogue on sustainability." Although the debate was not designed to achieve a definitive sustainability policy for SETAC, the audience clearly rejected the proposition, indicating a desire from the SETAC membership for an expanded role in global sustainability forums. This commentary details the key elements of the session, identifies the contribution the Advisory Group will have at the World Congress in Berlin (May 2012), and invites interested persons to become active in the Advisory Group.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.302
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2012
Admission routes1
Has abstractyes

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